{smcl}
{com}{sf}{ul off}{txt}{.-}
       log:  {res}C:\brian\research\autho\ajps\ajpsreplication.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}19 Aug 2005, 12:57:58
{txt}
{com}. ** replicate model 1
. nbreg    initone initonelag total  cap open totalally     party military, cluster(ccode)

{txt}Fitting Poisson model:

Iteration 0:   log pseudo-likelihood = {res}-8992.3878{txt}  
Iteration 1:   log pseudo-likelihood = {res}-6697.3198{txt}  (backed up)
Iteration 2:   log pseudo-likelihood = {res}-4635.2817{txt}  
Iteration 3:   log pseudo-likelihood = {res} -2955.843{txt}  
Iteration 4:   log pseudo-likelihood = {res} -2677.538{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2672.9736{txt}  
Iteration 6:   log pseudo-likelihood = {res}-2672.9558{txt}  
Iteration 7:   log pseudo-likelihood = {res}-2672.9558{txt}  

Fitting constant-only model:

Iteration 0:   log pseudo-likelihood = {res}-3040.8648{txt}  
Iteration 1:   log pseudo-likelihood = {res}-3005.7721{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2996.4754{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2996.4754{txt}  

Fitting full model:

Iteration 0:   log pseudo-likelihood = {res}-2735.9194{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2632.7938{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2614.9855{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2612.5859{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2612.5463{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2612.5463{txt}  

Negative binomial regression{col 51}Number of obs{col 67}= {res}      4960
{col 51}{txt}Wald chi2({res}7{txt}){col 67}= {res}    135.13
{txt}Log pseudo-likelihood = {res}-2612.5463{col 51}{txt}Prob > chi2{col 67}= {res}    0.0000

                            {txt}(standard errors adjusted for clustering on ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
     initone {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
  initonelag {c |}  {res} .5492478   .0965914     5.69   0.000     .3599323    .7385634
       {txt}total {c |}  {res} .0704553   .0185465     3.80   0.000     .0341049    .1068057
         {txt}cap {c |}  {res} 4.676322    1.61411     2.90   0.004     1.512725    7.839919
        {txt}open {c |}  {res}-.9466104   .2833079    -3.34   0.001    -1.501884   -.3913372
   {txt}totalally {c |}  {res} .0072639   .0057708     1.26   0.208    -.0040467    .0185745
       {txt}party {c |}  {res} .1246201   .2070777     0.60   0.547    -.2812448     .530485
    {txt}military {c |}  {res}  .516266    .224477     2.30   0.021     .0762992    .9562327
       {txt}_cons {c |}  {res}-2.173253   .2801637    -7.76   0.000    -2.722364   -1.624143
{txt}{hline 13}{c +}{hline 64}
    /lnalpha {c |}  {res}-.3081515   .2007676{col 58}-.7016487    .0853457
{txt}{hline 13}{c +}{hline 64}
       alpha {c |}  {res}  .734804   .1475248{col 58} .4957673    1.089094
{txt}{hline 13}{c BT}{hline 64}

{com}. ** replicate model 2
. nbreg    majorinitone majorinitonelag total  cap open totalally     party military, cluster(ccode)

{txt}Fitting Poisson model:

Iteration 0:   log pseudo-likelihood = {res}-11229.253{txt}  
Iteration 1:   log pseudo-likelihood = {res}-8575.3463{txt}  (backed up)
Iteration 2:   log pseudo-likelihood = {res}-5784.3953{txt}  (backed up)
Iteration 3:   log pseudo-likelihood = {res}-3740.2983{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2809.2395{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2224.0174{txt}  
Iteration 6:   log pseudo-likelihood = {res}-2127.1354{txt}  
Iteration 7:   log pseudo-likelihood = {res}-2122.8768{txt}  
Iteration 8:   log pseudo-likelihood = {res}-2122.8503{txt}  
Iteration 9:   log pseudo-likelihood = {res}-2122.8503{txt}  

Fitting constant-only model:

Iteration 0:   log pseudo-likelihood = {res}-2377.7441{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2346.4968{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2346.2821{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2346.2818{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2346.2818{txt}  

Fitting full model:

Iteration 0:   log pseudo-likelihood = {res}-2152.9589{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2081.2339{txt}  
Iteration 2:   log pseudo-likelihood = {res}  -2073.71{txt}  
Iteration 3:   log pseudo-likelihood = {res} -2073.466{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2073.4654{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2073.4654{txt}  

Negative binomial regression{col 51}Number of obs{col 67}= {res}      4960
{col 51}{txt}Wald chi2({res}7{txt}){col 67}= {res}    142.16
{txt}Log pseudo-likelihood = {res}-2073.4654{col 51}{txt}Prob > chi2{col 67}= {res}    0.0000

                            {txt}(standard errors adjusted for clustering on ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
majorinitone {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
majorinito~g {c |}  {res} .7084981   .1149432     6.16   0.000     .4832136    .9337827
       {txt}total {c |}  {res}  .070939   .0189776     3.74   0.000     .0337437    .1081343
         {txt}cap {c |}  {res} 4.041012   1.906321     2.12   0.034     .3046917    7.777333
        {txt}open {c |}  {res}-1.076493   .3464044    -3.11   0.002    -1.755433   -.3975526
   {txt}totalally {c |}  {res} .0050837   .0064837     0.78   0.433    -.0076242    .0177916
       {txt}party {c |}  {res} .2556285    .233801     1.09   0.274    -.2026131    .7138701
    {txt}military {c |}  {res} .6824639    .263488     2.59   0.010     .1660369    1.198891
       {txt}_cons {c |}  {res}-2.538105   .3000418    -8.46   0.000    -3.126176   -1.950034
{txt}{hline 13}{c +}{hline 64}
    /lnalpha {c |}  {res}-.0379111   .2217899{col 58}-.4726113     .396789
{txt}{hline 13}{c +}{hline 64}
       alpha {c |}  {res} .9627985   .2135389{col 58} .6233724    1.487042
{txt}{hline 13}{c BT}{hline 64}

{com}. ** replicate model 3
. nbreg    initone initonelag total  cap open totalally      machine junta boss strongman, cluster(ccode)

{txt}Fitting Poisson model:

Iteration 0:   log pseudo-likelihood = {res}-8980.8869{txt}  
Iteration 1:   log pseudo-likelihood = {res}-5923.3304{txt}  (backed up)
Iteration 2:   log pseudo-likelihood = {res}-4018.7065{txt}  
Iteration 3:   log pseudo-likelihood = {res}-3511.7698{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2696.7874{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2670.0756{txt}  
Iteration 6:   log pseudo-likelihood = {res}-2669.6386{txt}  
Iteration 7:   log pseudo-likelihood = {res}-2669.6384{txt}  

Fitting constant-only model:

Iteration 0:   log pseudo-likelihood = {res}-3040.8648{txt}  
Iteration 1:   log pseudo-likelihood = {res}-3005.7721{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2996.4754{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2996.4754{txt}  

Fitting full model:

Iteration 0:   log pseudo-likelihood = {res}-2736.3353{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2632.7918{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2613.9896{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2611.5101{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2611.4669{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2611.4669{txt}  

Negative binomial regression{col 51}Number of obs{col 67}= {res}      4960
{col 51}{txt}Wald chi2({res}9{txt}){col 67}= {res}    144.57
{txt}Log pseudo-likelihood = {res}-2611.4669{col 51}{txt}Prob > chi2{col 67}= {res}    0.0000

                            {txt}(standard errors adjusted for clustering on ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
     initone {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
  initonelag {c |}  {res} .5478616   .0988712     5.54   0.000     .3540776    .7416456
       {txt}total {c |}  {res} .0709149   .0179949     3.94   0.000     .0356456    .1061842
         {txt}cap {c |}  {res} 4.710765   1.566344     3.01   0.003     1.640787    7.780742
        {txt}open {c |}  {res}-.9585214   .2884076    -3.32   0.001     -1.52379    -.393253
   {txt}totalally {c |}  {res} .0072348   .0057766     1.25   0.210    -.0040871    .0185566
     {txt}machine {c |}  {res} .0598636   .2340011     0.26   0.798    -.3987702    .5184974
       {txt}junta {c |}  {res} .5352655   .2669429     2.01   0.045     .0120671    1.058464
        {txt}boss {c |}  {res} .2014298   .2134062     0.94   0.345    -.2168386    .6196981
   {txt}strongman {c |}  {res} .5127864   .2392232     2.14   0.032     .0439175    .9816553
       {txt}_cons {c |}  {res}-2.171972   .2760412    -7.87   0.000    -2.713003   -1.630941
{txt}{hline 13}{c +}{hline 64}
    /lnalpha {c |}  {res} -.319951   .2104563{col 58}-.7324378    .0925358
{txt}{hline 13}{c +}{hline 64}
       alpha {c |}  {res} .7261846   .1528301{col 58} .4807356    1.096952
{txt}{hline 13}{c BT}{hline 64}

{com}. ** replicate model 4
. nbreg    majorinitone majorinitonelag total  cap open totalally      machine junta boss strongman, cluster(ccode)

{txt}Fitting Poisson model:

Iteration 0:   log pseudo-likelihood = {res} -11227.37{txt}  
Iteration 1:   log pseudo-likelihood = {res}-8175.4339{txt}  (backed up)
Iteration 2:   log pseudo-likelihood = {res}-6846.8022{txt}  (backed up)
Iteration 3:   log pseudo-likelihood = {res}-5688.7112{txt}  
Iteration 4:   log pseudo-likelihood = {res}-3844.8737{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2517.1606{txt}  
Iteration 6:   log pseudo-likelihood = {res}-2131.2652{txt}  
Iteration 7:   log pseudo-likelihood = {res}-2119.6586{txt}  
Iteration 8:   log pseudo-likelihood = {res}-2119.5096{txt}  
Iteration 9:   log pseudo-likelihood = {res}-2119.5096{txt}  

Fitting constant-only model:

Iteration 0:   log pseudo-likelihood = {res}-2377.7441{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2346.4968{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2346.2821{txt}  
Iteration 3:   log pseudo-likelihood = {res}-2346.2818{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2346.2818{txt}  

Fitting full model:

Iteration 0:   log pseudo-likelihood = {res} -2153.038{txt}  
Iteration 1:   log pseudo-likelihood = {res}-2080.4566{txt}  
Iteration 2:   log pseudo-likelihood = {res}-2072.1562{txt}  
Iteration 3:   log pseudo-likelihood = {res}  -2071.88{txt}  
Iteration 4:   log pseudo-likelihood = {res}-2071.8792{txt}  
Iteration 5:   log pseudo-likelihood = {res}-2071.8792{txt}  

Negative binomial regression{col 51}Number of obs{col 67}= {res}      4960
{col 51}{txt}Wald chi2({res}9{txt}){col 67}= {res}    157.75
{txt}Log pseudo-likelihood = {res}-2071.8792{col 51}{txt}Prob > chi2{col 67}= {res}    0.0000

                            {txt}(standard errors adjusted for clustering on ccode)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
majorinitone {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
majorinito~g {c |}  {res} .7041516   .1212837     5.81   0.000       .46644    .9418632
       {txt}total {c |}  {res} .0714469   .0183654     3.89   0.000     .0354514    .1074424
         {txt}cap {c |}  {res} 4.109179   1.812977     2.27   0.023     .5558085     7.66255
        {txt}open {c |}  {res} -1.09312   .3535128    -3.09   0.002    -1.785992   -.4002475
   {txt}totalally {c |}  {res} .0050797   .0064738     0.78   0.433    -.0076087     .017768
     {txt}machine {c |}  {res} .1640999   .2572962     0.64   0.524    -.3401914    .6683911
       {txt}junta {c |}  {res} .6451821   .2791478     2.31   0.021     .0980624    1.192302
        {txt}boss {c |}  {res} .3626842   .2399143     1.51   0.131    -.1075392    .8329077
   {txt}strongman {c |}  {res} .6927977   .2802467     2.47   0.013     .1435242    1.242071
       {txt}_cons {c |}  {res}-2.536204   .2946976    -8.61   0.000    -3.113801   -1.958608
{txt}{hline 13}{c +}{hline 64}
    /lnalpha {c |}  {res} -.050397    .228234{col 58}-.4977275    .3969334
{txt}{hline 13}{c +}{hline 64}
       alpha {c |}  {res} .9508518   .2170167{col 58} .6079106    1.487257
{txt}{hline 13}{c BT}{hline 64}

{com}. log close
       {txt}log:  {res}C:\brian\research\autho\ajps\ajpsreplication.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}19 Aug 2005, 12:59:11
{txt}{.-}
{smcl}
{txt}{sf}{ul off}